We adopt COCO-OCC split to compare the occlusion handling ability of BCNet with other methods on images with highly overlapping objects. As shown in Table REF , our BCNet with Faster R-CNN detector has 31.71 AP vs. 30.32 for the Mask Scoring R-CNN [1]}. By further training BCNet on the synthetic occlusion dataset, the performance of AP and AP\(_{50}\)  is significantly promoted to 32.89 and 53.25 respectively, which shows the advantage brought by this new dataset.
